Systems and methods for mining model accuracy display for multiple state prediction

ABSTRACT

Systems and methods are provided for producing a mining model accuracy display that depicts the model&#39;s accuracy at predicting a state for a multiple-state variable. The model predicts a state and provides an associated probability for each case. Points are graphed such that one coordinate of the data point corresponds to a number N of cases and the other coordinate corresponds to the number of correct predictions made in the top N cases by probability.

FIELD OF THE INVENTION

The present invention relates to systems and methods for evaluating anddisplaying the reliability of a data mining model which predicts thestate of a variable with multiple possible states. More particularly,the present invention relates to systems and methods for displaying theaccuracy of one or more predictive models in a data mining context.

BACKGROUND OF THE INVENTION

Data mining is the exploration and analysis of large quantities of data,in order to discover correlations, patterns, and trends in the data.Data mining may also be used to create models that can be used topredict future data or classify existing data.

For example, a business may amass a large collection of informationabout its customers. This information may include purchasing informationand any other information available to the business about the customer.The predictions of a model associated with customer data may be used,for example, to control customer attrition, to perform credit-riskmanagement, to detect fraud, or to make decisions on marketing.

To create and test a data mining model, available data may be dividedinto two parts. One part, the training data set, may be used to createmodels. The rest of the data, the testing data set, may be used to testthe model, and thereby determine the accuracy of the model in makingpredictions.

Data within data sets is grouped into cases. For example, with customerdata, each case corresponds to a different customer. Data in the casedescribes or is otherwise associated with chat customer. One type ofdata that may be associated with a case (for example, with a givencustomer) is a categorical variable. A categorical variable categorizesthe case into one of several pre-defined states. For example, one suchvariable may correspond to the educational level of the customer. Thereare various values for this variable. The possible values are known asstates. For instance, the states of the educational level variable maybe “high school degree,” “bachelor's degree,” or “graduate degree” andmay correspond to the highest degree earned by the customer.

As mentioned, available data is partitioned into two groups—a trainingdata set and a testing data set. Often 70% of the data is used fortraining and 30% for testing. A model may be trained on the trainingdata set, which includes this information. Once a model is trained, itmay be run on the testing data set for evaluation. During this testing,the model will be given all of the data except the educational leveldata, and asked to predict a probability that the educational levelvariable for that customer is “bachelor's degree”.

Running the model on the testing data set, these results are compared tothe actual testing data to see whether the model correctly predicted ahigh probability of the “bachelor's degree” state for cases thatactually have “bachelor's degree” as the state of the educational levelvariable. One method of displaying the success of a model graphically isby means of a lift char, also known as a cumulative gains char. Tocreate a lift char, the cases from the testing data set are sortedaccording to the probability assigned by the model that the variable(e.g. educational level) has the state (e.g. bachelor's degree) that wastested, from highest probability to lowest probability. Once this isdone, a lift chart can be created from data points (X, Y) showing foreach point what number Y of the total number of true positives (thosecases where the variable does have the state being tested for) areincluded in the X % of the testing data set cases with the highestprobability for that state, as assigned by the model.

As shown in FIG. 1, the conventional lift chart shows that there are1000 total true positives in the testing set. This is not necessarilythe number of cases in the testing data set. Some cases may have adifferent state for the variable than the one for which the test isbeing conducted. The number of true positives in the testing data set isthe highest number shown on Y axis 10. The X axis 20 correlates with thepercentage of cases with the highest probabilities. Lift line 30 depictsthe success of the model. For example, it can be seen that lift line 30includes a point with (X, Y) coordinates are approximately (20, 500).This indicates that, in the 20% of the cases selected by the model asthe most probable cases having the tested-for state of the variable,approximately 500 of the cases that are truly positive for the state ofthe variable are included. This is equivalent to getting 50% of theactual cases with the desired state in only 20% of the cases for whichthe test is conducted.

A model that randomly assigns probabilities would be likely to have achart close to the random lift line 40. In the top 10% of cases, such amodel would find 10% of the true positives. Note that the X axis mayalso be expressed in the number of high probability cases, and the Yaxis in percentages. A perfect model may also be considered. In asituation where there are N % true positives among the entire testingdata set, the lift line would stretch straight from the origin to thepoint (N, Y_(MAX)) (where Y_(MAX) is the maximum Y value). This isbecause all of the true positives would be identified before any falsepositives are identified. The lift line for the perfect model would thencontinue horizontally from that point to the right. For example, if 20%of the cases had the tested for stale, as shown in FIG. 2, a perfectmodel would have the perfect lift line 50, extending from (0,0) to (20,1000) and then from (20, 1000) to (100, 1000). Similarly the worst casemodel would identify no true positives until the last N % of tee testingpopulation is included, and, as shown in FIG. 3 for the case where thereare 20% true positives, the worst case lift line 60 for such a modelwould extend from (0,0) to (80, 0) and then straight from (80,0) to(100, 1000).

As described above, in the prior art, a lift cha can be used to displayand measure the prediction accuracy of a model for a given state of avariable. However, existing lift charts have several drawbacks. Becausemodels often must pick, for a given case and variable, what state ismost probable for that variable, it is useful to know with what accuracythey can perform this prediction for all states. Lift charts in theprior art do not allow the display of the general prediction accuracy ofa model for a variable over all states. A model may perform well for onestate but may perform badly when predicting another. Also, in the priorart, comparing lift charts for different models to evaluate theeffectiveness over the entire population will not be useful, as allconventional lift chart lines have a Y value corresponding to all of thetrue positives at the X value corresponding to the entire population ofthe testing set. This is because when all cases are considered, all truepositives are included.

Thus, there is a need for improved charts with which to understand tebehavior of models for predicting the state of multiple-state variables.

SUMMARY OF THE INVENTION

In view of the foregoing, the present invention provides systems andmethods for creating a chart displaying the accuracy of a model inpredicting the state of a multi-state variable. The most likely state ofthe variable for each input case is predicted, as well as its associatedprobability. The input cases are sorted based on the predictedprobabilities, regardless of which state of the variable was predicted.A chart is created which graphs the percentage of correct predictionsfor the most probable cases.

Other features and embodiments of the present invention are describedbelow.

BRIEF DESCRIPTION OF THE DRAWINGS

The system and methods for model accuracy display for multiple stateprediction in accordance with the present invention are furtherdescribed with reference to the accompanying drawings in which:

FIG. 1 is a lift chart according to the prior art with an exemplarymodel lift line and a random lift line depicted.

FIG. 2 is a lift chart according to the prior art with an exemplarymodel lift line and an ideal lift line depicted.

FIG. 3 is a lift chart according to the prior art with an exemplarymodel lift line and a worst case lift line depicted.

FIG. 4 is a block diagram of an exemplary computing environment in whichaspects of the invention may be implemented.

FIG. 5 is a chart according to the present invention with a multi-stateprediction evaluation line and an ideal multi-state prediction linedepicted.

FIG. 6 is a chart according to the present invention with a multi-stateprediction evaluation line and a random multi-state prediction linedepict

DETAILED DESCRIPTION OF THE INVENTION

Overview

As described in the background, the conventional lift chart can onlydisplay the effectiveness of a model at predicting one state of amulti-state variable. A method and system are presented for providing,across all possible states, a display of the accuracy of a data-miningmodel predicting the state of a multiple-state variable. A state isselected by the model for a variable found in the cases in the testingdata set. The model also provides an associated probability for eachcase. The cases are sorted by the associated probability set by themodel and the predicted state is compared to the actual state of thevariable in each case. The percentage of correct predictions among thehighest-probability cases is graphed.

Exemplary Computing Environment

FIG. 4 illustrates an example of a suitable computing system environment100 in which the invention may be implemented. The computing systemenvironment 100 is only one example of a suitable computing environmentand is not intended to suggest any limitation as to the scope of use orfunctionality of the invention. Neither should the computing environment100 be interpreted as having any dependency or requirement relating toany one or combination of components illustrated in the exemplaryoperating environment 100.

One of ordinary skill in the art can appreciate that a computer or otherclient or server device can be deployed as part of a computer network,or in a distributed computing environment. In this regard, the presentinvention pertains to any computer system having any number of memory orstorage units, and any number of applications and processes occurringacross any number of storage units or volumes, which may be used inconnection with the present invention. The present invention may applyto an environment with server computers and client computers deployed ina network environment or distributed computing environment, havingremote or local storage. The present invention may also be applied tostandalone computing devices, having pro ng language functionality,interpretation and execution capabilities for generating, receiving andtransmitting information in connection with remote or local services.

The invention is operational with numerous other general purpose orspecial purpose computing system environments or configurations.Examples of well known computing systems, environments, and/orconfigurations that may be suitable for use with the invention include,but are not limited to, personal computers, server computers, hand-heldor laptop devices, multiprocessor systems, microprocessor-based systems,set top boxes, programmable consumer electronics, network PCs,minicomputers, mainframe computers, distributed computing environmentsthat include any of the above systems or devices, and the like.

The invention may be described in the general context ofcomputer-executable instructions, such as program modules, beingexecuted by a computer. Generally, program modules include routines,programs, objects, components, data structures, etc. that performparticular tasks or implement particular abstract data types. Theinvention may also be practiced in distributed computing environmentswhere tasks are performed by remote processing devices that are linkedthrough a communications network or other data transmission medium. In adistributed computing environment, program modules and other data may belocated in both local and remote computer storage media including memorystorage devices. Distributed computing facilitates sharing of computerresources and services by direct exchange between computing devices andsystems. These resources and services include the exchange ofinformation, cache storage, and disk storage for files. Distributedcomputing takes advantage of network connectivity, allowing clients toleverage their collective power to benefit the entire enterprise. Inthis regard, a variety of devices may have applications, objects orresources that may utilize the techniques of the present invention.

With reference to FIG. 4, an exemplary system for implementing theinvention includes a general-purpose computing device in the form of acomputer 110. Components of computer 110 may include, but are notlimited to, a processing unit 120, a system memory 130, and a system bus121 that couples various system components including the system memoryto the processing unit 120. The system bus 121 may be any of severaltypes of bus structures including a memory bus or memory controller, aperipheral bus, and a local bus using any of a variety of busarchitectures. By way of example, and not limitation, such architecturesinclude Industry Standard Architecture (ISA) bus, Micro ChannelArchitecture (MCA) bus, Enhanced ISA (EISA) bus, Video ElectronicsStandards Association (VESA) local bus, and Peripheral ComponentInterconnect (PCI) bus (also known as Mezzanine bus).

Computer 110 typically includes a variety of computer readable media.Computer readable media can be any available media that can be accessedby computer 110 and includes both volatile and nonvolatile media,removable and non-removable media. By way of example, and notlimitation, computer readable media may comprise computer storage mediaand communication media. Computer storage media includes both volatileand nonvolatile, removable and non-removable media implemented in anymethod or technology for storage of information such as computerreadable instructions, data structures, program modules or other data.Computer storage media includes, but is not limited to, RAM, ROM,EEPROM, flash memory or other memory technology, CDROM, digitalversatile disks (DVD) or other optical disk storage, magnetic cassettes,magnetic tape, magnetic disk storage or other magnetic storage devices,or any other medium that can be used to store the desired informationand that can accessed by computer 110. Communication media typicallyembodies computer readable instructions, data structures, programmodules or other data in a modulated data signal such as a carrier waveor other transport mechanism and includes any information deliverymedia. The term “modulated data signal” means a signal that has one ormore of its characteristics set or changed in such a manner as to encodeinformation in the signal. By way of example, and not limitation,communication media includes wired media such as a wired network ordirect-wired connection, and wireless media such as acoustic, RF,infrared and other wireless media. Combinations of any of the aboveshould also be included within the scope of computer readable media.

The system memory 130 includes computer storage media in the form ofvolatile and/or nonvolatile memory such as read only memory (ROM) 131and random access memory (RAM) 132. A basic input/output system 133(BIOS), containing the basic routines that help to transfer informationbetween elements within computer 110, such as during sign-up, istypically stored in ROM 131. RAM 132 typically contains data and/orprogram modules that are immediately accessible to and/or presentlybeing operated on by processing unit 120. By way of example, and notlimitation, FIG. 4 illustrates operating system 134, applicationprograms 135, other program modules 136, and program data 137.

The computer 110 may also include other removable/non-removable,volatile/nonvolatile computer storage media. By way of example only,FIG. 4 illustrates a hard disk drive 140 that reads from or writes tonon-removable, nonvolatile magnetic media, a magnetic disk drive 151that reads from or writes to a removable, nonvolatile magnetic disk 152,and an optical disk drive 155 that reads from or writes to a removable,nonvolatile optical disk 156, such as a CD ROM or other optical media.Other removable/non-removable, volatile/nonvolatile computer storagemedia that can be used in the exemplary operating environment include,but are not limited to, magnetic tape cassettes, flash memory cards,digital versatile disks, digital video tape, solid state RAM, solidstate ROM, and the like. The hard disk drive 141 is typically connectedto the system bus 121 through an non-removable memory interface such asinterface 140, and magnetic disk drive 151 and optical disk drive 155are typically connected to the system bus 121 by a removable memoryinterface, such as interface 150.

The drives and their associated computer storage media discussed aboveand illustrated in FIG. 4, provide storage of computer readableinstructions, data structures, program modules and other data for thecomputer 110. In FIG. 4, for example, hard disk drive 141 is illustratedas storing operating system 144, application programs 145, other programmodules 146, and program data 147. Note that these components can eitherbe the same as or different from operating system 134, applicationprograms 135, other program modules 136, and program data 137. Operatingsystem 144, application programs 145, other program modules 146, andprogram data 147 are given different numbers here to illustrate that, ata minimum, they are different copies. A user may enter commands andinformation into the computer 20 through input devices such as akeyboard 162 and pointing device 161, commonly referred to as a mouse,trackball or touch pad. Other input devices (not shown) may include amicrophone, joystick, game pad, satellite dish, scanner, or the like.These and other input devices are often connected to the processing unit120 through a user input interface 160 that is coupled to the systembus, but may be connected by other interface and bus structures, such asa parallel port, game port or a universal serial bus (USB). A monitor191 or other type of display device is also connected to the system bus121 via an interface, such as a video interface 190. In addition to themonitor, computers may also include other peripheral output devices suchas speakers 197 and printer 196, which may be connected through anoutput peripheral interface 190.

The computer 110 may operate in a networked environment using logicalconnections to one or more remote computers, such as a remote computer180. The remote computer 180 may be a personal computer, a server, arouter, a network PC, a peer device or other common network node, andtypically includes many or all of the elements described above relativeto the computer 110, although only a memory storage device 181 has beenillustrated in FIG. 4. The logical connections depicted in FIG. 4include a local area network (LAN) 171 and a wide area network (WAN)173, but may also include other networks. Such networking environmentsare commonplace in offices, enterprise-wide computer networks, intranetsand the Internet.

When used in a LAN networking environment, the computer 110 is connectedto the LAN 171 through a network interface or adapter 170. When used ina WAN networking environment, the computer 110 typically includes amodem 172 or other means for establishing communications over the WAN173, such as the Internet. The modem 172, which may be internal orexternal, may be connected to the system bus 121 via the user inputinterface 160, or other appropriate mechanism. In a networkedenvironment, program modules depicted relative to the computer 110, orportions thereof, may be stored in the remote memory storage device. Byway of example, and not limitation, FIG. 4 illustrates remoteapplication programs 185 as residing on memory device 181. It will beappreciated that the network connections shown are exemplary and othermeans of establishing a communications link between the computers may beused.

Calculation and Display of Multi-State Prediction Evaluation

In order to calculate and display an evaluation of the success of amodel in predicting a multi-state variable, the inventive techniquecompares the predictions made on a testing set of data to the actualstate of the variable, known for all cases in the testing set.

Unlike the process of preparing a conventional lift chart, for each casethe model provides the state with the highest probability and thatassociated probability, for the given variable. For example, considerthe data set where the cases are customers, variable is educationallevel, and the states are “high school degree,” “bachelors degree,” and“graduate degree.” The request to the model will be to provide mostprobable state for the educational level variable, and the probabilitythat that state is correct.

Thus, information, for each case, about the predicted state of thevariable and the associated probability can be gathered. Table 1, below,shows an abbreviated version of a table with this information. In thattable, M customer cases are included in the training data. TABLE 1Customer Cases, Predicted Educational Level, and Associated ProbabilityPredicted State of Educational Level Customer Variable Probability 1bachelor's degree .510 2 graduate degree .929 3 bachelor's degree .745 4high school degree .767 5 high school degree .463 6 bachelor's degree .. . . . . . . . M graduate degree .561

Once this table has been completed, it can be sorted by probability, andthe information such as the one in Table 2 is created. TABLE 2 CustomerCases, Predicted Educational Level, and Associated Probability PredictedState of Educational Level Customer Variable Probability 225 graduatedegree .933 871 graduate degree .932 125 bachelor's degree .931 403 highschool degree .930 677 bachelor's degree .930 2 graduate degree .929 . .. . . . . . . M bachelor's degree .338

With this information, it is possible to examine cases by the level ofcertainty of the model. The computer can determine, for some percentageX, what cases are in the top X % of the training data set cases rankedby the associated probability the model has assigned. And, havingdetermined what those cases are, the computer can determine, byconsulting the actual value of the multi-state variable for the cases inthe training data set, what percentage Y of the total training data setwas predicted correctly by the model. Graphing these X and Y valuesyields a display of the accuracy of the model on multi-state predictionover all states.

FIG. 5 shows such a multi-state prediction evaluation display. X axis520 corresponds to the percentage of total cases being considered. Thesecases ae the cases to which the model has assigned the highestprobability of correctness of the model's selected state. Y axis 510corresponds to the percentage of correct identifications of the testingdata set contained within the cases being examined. Multi-stateprediction evaluation line 530 is an exemplary multi-state predictionevaluation line. This line represents at point A that for the 20% of thetesting data set for which the model was the most certain, the model hadperfect accuracy, with 20% of the testing data set being identifiedcorrectly within that first 20% of the model's predictions. However, themodel's accuracy decreases as the associated probability of the guessesdecreases, and point B represents that when the entire set ofpredictions is considered (where X=100) the model identifies the correctstate for approximately only 60% of the cases in the testing data set(Y=60).

FIG. 5 also includes the ideal multi-state prediction evaluation line540. This line indicates that a perfect model would identify 20% of thetesting data set correctly in the top 20% most certain predictions, 50%in the top 50%, and 100% in the top 100%. The worst-case multi-stateprediction evaluation line would never get any of the state predictionscorrect, and it would lie overlapping the X axis. A multi-stateprediction evaluation line for a model that selects states randomly canbe seen as random multi-state prediction evaluation line 550 in FIG. 6.This random multi-state prediction line is for a variable with 3possible states. At point C, it can be seen that when 100% of the casesare considered, such a model is likely to get approximately {fraction(1/3)} of them correct.

It can be seen in FIG. 6 that the random multi-state predictionevaluation line 550 indicates that the random model has constantaccuracy over all of the data Multi-state prediction evaluation line540, on the other hand, indicates that the model to which it correspondshas a higher success rate for the cases to which it had assigned ahigher probability of correctness. When comparing point D to point B,the multi-state prediction evaluation line 540 shows that the model'ssuccess rate with 80% of the cases considered is similar to that with100% considered. A model is possible that has a constant rate ofsuccess, regardless of the associated probability the model assigns tocorrectness of the state it has selected for the multi-state variable.It is, of course, also possible that a model correctly performs betteron cases to which it assigns a lower associated probability. All ofthese situations can be represented with multi-state predictionevaluation lines according to the invention.

More than one multi-state prediction evaluation lines may be displayedon a single display. This is useful, for example, in order to comparethe accuracy of different models, or, in cases where there are multipletesting data sets with different characteristics, to compare theaccuracy of a single model on the different testing data sets.

Additionally, the display may be customized to user specifications. If auser wished to only see te accuracy of the model over a specific rangeof the testing set—for example, if the user wished to only see theaccuracy of the model on the cases for which the associated probabilityof correctness was among the top half of the sorted probabilities, asection of the chart may be presented. Additionally, the relative scaleof the axes could be modified. The axes could be changed to displaynumber of cases rather than percentage. The graph could also be modifiedto display the difference between two models in the Y value rather thandisplaying each of the two models.

The multi-state prediction evaluation line may be produced usingapproximations. For example, where there are 10,000 cases in the testingdata set, it may be that the line may be produced by examining the topone hundred cases (by associated probability), then the top two hundredcases, then the top three hundred cases, etc., instead of evaluating theaccuracy with the top case, the top two cases, the top three cases, etc.In this way, computational time may be saved for a small cost inaccuracy. Not all points (X, Y) on the line must be exact, and the linemay be produced using prior art algorithms for creating a representativeline from data points. In place of lines, data points may be displayed.Equivalent graphs may be produced as is known in the prior art, bychanging the scale of the axes, or by changing the position of the axes.

These and other possible variations that would be obvious to one skilledin the art are contemplated, and the invention should not be limited toany single embodiment.

CONCLUSION

Herein a system and method for mining model accuracy display formultiple state prediction is provided that produces a display bygraphing the percentage of the total population for which the correctstate was selected by the mining model in a specified percentage of thecases in the data tested. The specified percentage is selected from thedata by selecting those cases with the highest predicted probability ofcorrectness, as predicted by the model.

The invention also contemplates placing more than one multi-stateprediction evaluation lines on a single graph in order to compare andcontrast the accuracy of more than one model, or to compare and contrastthe accuracy of one model on several testing data sets, which may havebeen selected for various attributes of the data sets.

As mentioned above, while exemplary embodiments of the present inventionhave been described in connection with various computing devices andnetwork architectures, the underlying concepts may be applied to anycomputing device or system in which it is desirable to have a display ofthe accuracy of a data mining model over multiple states of amulti-state variable. Thus, the techniques for providing such a displayin accordance with the present invention may be applied to a variety ofapplications and devices. For instance, the algorithm(s) of theinvention may be applied to the operating system of a computing device,provided as a separate object on the device, as part of another object,as a downloadable object from a server, as a “middle man” between adevice or object and the network, as a distributed object, etc. Whileexemplary programming languages, names and examples are chosen herein asrepresentative of various choices, these languages, names and examplesare not intended to be limiting. One of ordinary skill in the art willappreciate there are numerous ways of providing object code thatachieves the same, similar or equivalent parametrization achieved by theinvention.

The various techniques described her may be implemented in connectionwith hardware or software or, where appropriate, with a combination ofboth. Thus, the methods and apparatus of the present invention, orcertain aspects or portions thereof, may take the form of program code(i.e., instructions) embodied in tangible media, such as floppydiskettes, CD-ROMs, hard drives, or any other machine-readable storagemedium, wherein, when the program code is loaded into and executed by amachine, such as a computer, the machine becomes an apparatus forpracticing the invention. In the case of program code execution onprogrammable computers, the computing device will generally include aprocessor, a storage medium readable by the processor (includingvolatile and non-volatile memory and/or storage elements), at least oneinput device, and at least one output device. One or more programs thatmay utilize the techniques of the present invention, e.g., through theuse of a data processing API or the like, are preferably implemented ina high level procedural or object oriented programming language tocommunicate with a computer system. However, the program(s) can beimplemented in assembly or machine language, if desired. In any case,the language may be a compiled or interpreted language, and combinedwith hardware implementations.

The methods and apparatus of the present invention may also be practicedvia communications embodied in the form of program code that istransmitted over some transmission medium, such as over electricalwiring or cabling, through fiber optics, or via any other form oftransmission, wherein, when the program code is received and loaded intoand executed by a machine, such as an EPROM, a gate array, aprogrammable logic device (PLD), a client computer, a video recorder orthe like, or a receiving machine having the signal processingcapabilities as described in exemplary embodiments above becomes anapparatus for practicing the invention. When implemented on ageneral-purpose processor, the program code combines with the processorto provide a unique apparatus that operates to invoke the functionalityof the present invention. Additionally, any storage techniques used inconnection with the present invention may invariably be a combination ofhardware and software.

While the present invention has been described in connection with thepreferred embodiments of the various figures, it is to be understoodthat other similar embodiments may be used or modifications andadditions may be made to the described embodiment for performing thesame function of th present invention without deviating therefrom. Forexample, while exemplary network environments of the invention aredescribed in the context of a networked environment, such as a peer topeer networked environment, one skilled in the arm will recognize thatthe present invention is not limited thereto, and that the methods, asdescribed in the present application may apply to any computing deviceor environment, such as a gaming console, handheld computer, portablecomputer, etc., whether wired or wireless, and may be applied to anynumber of such computing devices connected via a communications network,and interacting across the network. Furthermore, it should be emphasizedthat a variety of computer platforms, including handheld deviceoperating systems and other application specific operating systems arecontemplated, especially as the number of wireless networked devicescontinues to proliferate. Still further, the present invention may beimplemented in or across a plurality of processing chips or devices, andstorage may similarly be effected across a plurality of devices.Therefore, the present invention should not be limited to any singleembodiment, but rather should be construed in breadth and scope inaccordance with the appended claims.

1-28. Canceled.
 29. A computer-readable medium havingcomputer-executable instructions for accuracy display for a data miningmodel, where said model predicts, for each case in a testing data set, aspecific state from among a state set of at least two possible statesand where said model determines an associated probability for each ofsaid predictions, and where the correctness of said prediction isverifiable, the computer-executable instructions comprising: producing avisual representation of a multi-state prediction evaluation on a graphof at least two dimensions comprising at least two data points where foreach of said data points: (1) a data point set of said cases from amongsaid testing data set has been selected such that no case in said datapoint set has a lower associated probability for the prediction made bysaid model than any case not in said data point set, (2) a firstcoordinate of said data points corresponds to the number of cases insaid data point set, and (3) a second coordinate of said data pointscorresponds to the number of correct predictions for cases contained insaid data point set, wherein, for each of said data points, said firstcoordinate is equal to the percentage of said cases in said testing dataset which are contained in said data point set.
 30. A computer-readablemedium having computer-executable instructions for accuracy display fora data mining model, where said model predicts, for each case in atesting data set, a specific state from among a state set of at leasttwo possible states and where said model determines an associatedprobability for each of said predictions, and where the correctness ofsaid prediction is verifiable, said modules comprising: producing avisual representation of a multi-state prediction evaluation on a graphof at least two dimensions comprising at least two data points where foreach of said data points: (1) a data point set of said cases from amongsaid testing data set has been selected such that no case in said datapoint set has a lower associated probability for the prediction made bysaid model than any case not in said data point set, (2) a firstcoordinate of said data points corresponds to the number of cases insaid data point set, and (3) a second coordinate of said data pointscorresponds to the number of correct predictions for cases contained insaid data point set, wherein, for each of said data points, said secondcoordinate is equal to the percentage of said cases in said testing dataset for which said prediction associated with said cases contained insaid data point set is correct.
 31. A computer-readable medium havingcomputer-executable instructions for accuracy display for a data miningmodel, where said model predicts, for each case in a testing data set, aspecific state from among a state set of at least two possible statesand where said model determines an associated probability for each ofsaid predictions, and where the correctness of said prediction isverifiable, said modules comprising: producing a visual representationof a multi-state prediction evaluation on a graph of at least twodimensions comprising at least two data points where for each of saiddata points: (1) a data point set of said cases from among said testingdata set has been selected such that no case in said data point set hasa lower associated probability for the prediction made by said modelthan any case not in said data point set, (2) a first coordinate of saiddata points corresponds to the number of cases in said data point set,and (3) a second coordinate of said data points corresponds to thenumber of correct predictions for cases contained in said data pointset; and a module for producing, on said graph, the visualrepresentation of a multi-state prediction evaluation corresponding to arandom model.